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distilbert-classn-LinearAlg-finetuned-pred-span-width-5

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Classification Training

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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: dslim/distilbert-NER
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: distilbert-classn-LinearAlg-finetuned-pred-span-width-5
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # distilbert-classn-LinearAlg-finetuned-pred-span-width-5
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+
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+ This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6177
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+ - Accuracy: 0.8492
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+ - F1: 0.8483
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+ - Precision: 0.8737
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+ - Recall: 0.8492
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 4
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 30
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 5.1256 | 0.6849 | 50 | 2.4038 | 0.1270 | 0.0923 | 0.1177 | 0.1270 |
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+ | 5.0918 | 1.3699 | 100 | 2.3642 | 0.1508 | 0.1164 | 0.1424 | 0.1508 |
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+ | 4.9252 | 2.0548 | 150 | 2.3183 | 0.1905 | 0.1577 | 0.1813 | 0.1905 |
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+ | 4.871 | 2.7397 | 200 | 2.2588 | 0.1984 | 0.1741 | 0.2039 | 0.1984 |
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+ | 4.7135 | 3.4247 | 250 | 2.1740 | 0.3016 | 0.2812 | 0.3889 | 0.3016 |
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+ | 4.4839 | 4.1096 | 300 | 2.0073 | 0.3810 | 0.3573 | 0.4049 | 0.3810 |
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+ | 4.089 | 4.7945 | 350 | 1.8097 | 0.4762 | 0.4608 | 0.5123 | 0.4762 |
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+ | 3.7117 | 5.4795 | 400 | 1.6202 | 0.5952 | 0.5875 | 0.6159 | 0.5952 |
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+ | 3.0244 | 6.1644 | 450 | 1.4372 | 0.6508 | 0.6382 | 0.7022 | 0.6508 |
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+ | 2.6119 | 6.8493 | 500 | 1.2138 | 0.6746 | 0.6621 | 0.6894 | 0.6746 |
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+ | 2.0546 | 7.5342 | 550 | 1.0689 | 0.6984 | 0.6874 | 0.7277 | 0.6984 |
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+ | 1.5176 | 8.2192 | 600 | 0.9357 | 0.7619 | 0.7650 | 0.8379 | 0.7619 |
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+ | 1.1514 | 8.9041 | 650 | 0.8404 | 0.7937 | 0.7887 | 0.8468 | 0.7937 |
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+ | 0.8779 | 9.5890 | 700 | 0.7515 | 0.7698 | 0.7674 | 0.7985 | 0.7698 |
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+ | 0.5939 | 10.2740 | 750 | 0.6833 | 0.8016 | 0.8010 | 0.8354 | 0.8016 |
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+ | 0.475 | 10.9589 | 800 | 0.6577 | 0.8175 | 0.8159 | 0.8515 | 0.8175 |
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+ | 0.2866 | 11.6438 | 850 | 0.5898 | 0.8254 | 0.8239 | 0.8492 | 0.8254 |
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+ | 0.2275 | 12.3288 | 900 | 0.5941 | 0.8413 | 0.8401 | 0.8636 | 0.8413 |
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+ | 0.1275 | 13.0137 | 950 | 0.6122 | 0.8254 | 0.8285 | 0.8676 | 0.8254 |
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+ | 0.1359 | 13.6986 | 1000 | 0.5818 | 0.8254 | 0.8233 | 0.8464 | 0.8254 |
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+ | 0.0648 | 14.3836 | 1050 | 0.6180 | 0.8333 | 0.8326 | 0.8613 | 0.8333 |
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+ | 0.0865 | 15.0685 | 1100 | 0.5762 | 0.8413 | 0.8392 | 0.8629 | 0.8413 |
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+ | 0.0496 | 15.7534 | 1150 | 0.6235 | 0.8254 | 0.8223 | 0.8498 | 0.8254 |
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+ | 0.0237 | 16.4384 | 1200 | 0.5911 | 0.8413 | 0.8389 | 0.8560 | 0.8413 |
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+ | 0.0287 | 17.1233 | 1250 | 0.6150 | 0.8333 | 0.8319 | 0.8566 | 0.8333 |
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+ | 0.0181 | 17.8082 | 1300 | 0.6299 | 0.8333 | 0.8324 | 0.8577 | 0.8333 |
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+ | 0.0098 | 18.4932 | 1350 | 0.6181 | 0.8413 | 0.8399 | 0.8690 | 0.8413 |
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+ | 0.0182 | 19.1781 | 1400 | 0.5949 | 0.8413 | 0.8408 | 0.8590 | 0.8413 |
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+ | 0.0092 | 19.8630 | 1450 | 0.6146 | 0.8333 | 0.8324 | 0.8577 | 0.8333 |
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+ | 0.0051 | 20.5479 | 1500 | 0.6092 | 0.8413 | 0.8412 | 0.8632 | 0.8413 |
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+ | 0.0068 | 21.2329 | 1550 | 0.6137 | 0.8413 | 0.8412 | 0.8632 | 0.8413 |
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+ | 0.0044 | 21.9178 | 1600 | 0.6238 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0173 | 22.6027 | 1650 | 0.6199 | 0.8333 | 0.8324 | 0.8577 | 0.8333 |
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+ | 0.0037 | 23.2877 | 1700 | 0.5989 | 0.8333 | 0.8324 | 0.8577 | 0.8333 |
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+ | 0.0031 | 23.9726 | 1750 | 0.6229 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0032 | 24.6575 | 1800 | 0.6169 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0033 | 25.3425 | 1850 | 0.6022 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0054 | 26.0274 | 1900 | 0.6097 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0025 | 26.7123 | 1950 | 0.6182 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0029 | 27.3973 | 2000 | 0.6222 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0029 | 28.0822 | 2050 | 0.6148 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0027 | 28.7671 | 2100 | 0.6134 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+ | 0.0027 | 29.4521 | 2150 | 0.6177 | 0.8492 | 0.8483 | 0.8737 | 0.8492 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.3
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.3.0
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+ - Tokenizers 0.21.0
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